"""Prepare input artifacts expected by algorithm_subprocesses/5.15 main.py."""

from __future__ import annotations

import copy
import json
from dataclasses import dataclass
from pathlib import Path

import yaml

from app.algo_host.bundle import load_reference_default_config, resolve_reference_bundle_dir
from app.consumable_catalog import build_name_mapping


@dataclass(frozen=True)
class BatchJobFiles:
    config_path: Path
    excel_path: Path
    whitelist_path: Path
    output_path: Path
    work_dir: Path
    input_video_path: Path


def write_reference_catalog_excel(
    path: Path,
    *,
    candidate_consumables: list[str],
) -> None:
    import pandas as pd

    name_to_code = build_name_mapping(candidate_consumables)
    rows = [
        {
            "序号": idx,
            "产品编码": name_to_code.get(name, name),
            "商品名称": name,
        }
        for idx, name in enumerate(candidate_consumables, start=1)
    ]
    path.parent.mkdir(parents=True, exist_ok=True)
    pd.DataFrame(rows, columns=["序号", "产品编码", "商品名称"]).to_excel(path, index=False)


def write_reference_whitelist_json(path: Path, *, candidate_consumables: list[str]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(
        json.dumps({"allowed_names": candidate_consumables}, ensure_ascii=False, indent=2),
        encoding="utf-8",
    )


def build_job_config(
    *,
    bundle_dir: Path,
    video_path: Path,
    output_path: Path,
    work_dir: Path,
    excel_path: Path,
    whitelist_path: Path,
) -> dict:
    cfg = copy.deepcopy(load_reference_default_config(bundle_dir))
    cfg["io"]["video"] = str(video_path.resolve())
    cfg["io"]["excel"] = str(excel_path.resolve())
    cfg["io"]["out"] = str(output_path.resolve())
    cfg["io"]["whitelist_json"] = str(whitelist_path.resolve())
    cfg["runtime"]["work_dir"] = str(work_dir.resolve())
    cfg["runtime"]["keep_work_dir"] = False
    return cfg


def prepare_batch_job(
    *,
    bundle_dir: Path | None,
    cache_dir: Path,
    pipeline_video_path: Path,
    candidate_consumables: list[str],
) -> BatchJobFiles:
    root = resolve_reference_bundle_dir(bundle_dir)
    cache_output_dir = cache_dir / "output"
    cache_work_dir = cache_dir / "work"
    cache_config_dir = cache_dir / "config"
    for d in (cache_output_dir, cache_work_dir, cache_config_dir):
        d.mkdir(parents=True, exist_ok=True)

    pipeline_video = pipeline_video_path.resolve()
    output_path = cache_output_dir / "result.tsv"
    excel_path = cache_config_dir / "商品信息表.xlsx"
    whitelist_path = cache_config_dir / "whitelist.json"
    config_path = cache_config_dir / "config.yaml"

    write_reference_catalog_excel(excel_path, candidate_consumables=candidate_consumables)
    write_reference_whitelist_json(whitelist_path, candidate_consumables=candidate_consumables)
    config = build_job_config(
        bundle_dir=root,
        video_path=pipeline_video,
        output_path=output_path.resolve(),
        work_dir=cache_work_dir.resolve(),
        excel_path=excel_path.resolve(),
        whitelist_path=whitelist_path.resolve(),
    )
    config_path.write_text(
        yaml.safe_dump(config, allow_unicode=True, sort_keys=False),
        encoding="utf-8",
    )
    return BatchJobFiles(
        config_path=config_path,
        excel_path=excel_path,
        whitelist_path=whitelist_path,
        output_path=output_path,
        work_dir=cache_work_dir,
        input_video_path=pipeline_video,
    )